What is an “explanation” in face recognition? Explainable AI has explored various forms of explanation for machine learning systems in the form of: activation maximization [1]}, synthesizing optimal images [2]}, network attention [3]}[4]}[5]}, network dissection [6]} or synthesizing linguistic explanations [7]}. However, a key challenge in explainable AI is the lack of ground truth to compare and quantify explainable results across networks. XFR is especially challenging because the difference between near-mates or doppelgangers is subtle, the explanations are non-obvious, and differences are rarely well localized in a compact facial feature [8]}.
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